CN110135433A - A kind of representation data availability judgment method recommended based on vehicle - Google Patents
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Abstract
The present invention discloses a kind of representation data availability judgment method recommended based on vehicle, applied to Logistic Scheduling field, aiming at the problem that existing representation data lacks accuracy, the pretreatment that the present invention is drawn a portrait by vehicle data, data granulation, Information Granulating etc., from many levels, multiple dimensions classify to representation data, screening, analyze the data needed, and guarantee its accuracy, availability, and then when vehicle scheduling algorithm is when carrying out recommendation analytical calculation using these data, it can obtain more accurate recommendation results, to bring the significantly promotion of dispatching efficiency, reduce human factor intervention, by truck man, supplier of goods and dispatching platform tripartite comprehensive coordination；By improving the availability of vehicle data, further increases subsequent vehicle scheduling and recommend accuracy.
Description
Technical field
The invention belongs to Logistic Scheduling field, in particular to a kind of logistics representation data availability judgment technology.
Background technique
Representation data availability assessment is intended to improve the accuracy of data in vehicle and cargo scheduling system, is one kind to mention
High vehicle dispatch system availability is the advanced appraisal procedure of target, has used for reference the multidisciplinary research method taking human as object, weight
Depending on vehicle factor of the Technology application involved in, emphasize to be designed exploitation centered on vehicle.Representation data availability be
The quality of system is closely bound up, and quality here both includes system function, reliability etc., and includes the operating body of user
It tests.System developer's focus of attention is often the reality of the advance of technology, the reasonability of system architecture and system function
It is existing, and the accuracy of representation data is paid little attention to, the accuracy for paying little attention to can bring about system recommendation is inadequate, and user uses
It is poor to experience.The availability of recommender system is improved, reduces and calculates the time, makes data platform more load balancing, improves and recommends efficiency.
For vehicle dispatch system, having a critically important recommendation accuracy evaluation index is exactly the accurate of representation data
Property.Representation data accuracy is higher, illustrates that the vehicle all the more active in entire transportation system, recommends successful bigger, from
And it can carry out differentiating the availability of vehicle according to representation data；The vehicle not high for portrait accuracy is first given up, but
It is not to be deleted from database, it is possible to which the vehicle can be also added in the system in the future.
As application No. is method of drawing a portrait provided by 201810447891.3,201711450561.1 etc., fail very well
Embodiment representation data accuracy.
Summary of the invention
In order to solve the above technical problems, the present invention proposes a kind of representation data availability judgement side recommended based on vehicle
Method, the thought of the classification based on granularity representation data, from the consistency of data, integrality, accuracy, timeliness, entity unification
Property assesses the availability of representation data, to improve the accuracy of vehicle dispatch system.
A kind of the technical solution adopted by the present invention are as follows: representation data availability judgment method recommended based on vehicle, comprising:
S1, the logistics representation data of input is pre-processed, obtains the logistics representation data for complying with standard normal distribution；
S2, according to logistics representation data category division data grain；
S3, the mean value for calculating each data grain and the difference of two squares and；
S4, ascending sort is carried out to the mean value of each data grain, the equal value set an of ascending order is obtained, according to each data
The weighted average of the equal value set of the difference of two squares and calculating ascending order of grain, obtains orderly weighted average value set；
S5, according to orderly weighted average value set, calculate the corresponding confidence interval of data grain；
S6, classification information grain is converted for data grain according to confidence interval；
S7, according to classification information set discriminant function, obtain the availability of logistics representation data.
Further, step S1 specifically include it is following step by step:
S11, value type is converted by the character type data in the logistics representation data of input；
S12, shortage of data completion processing is carried out to through step S11 treated logistics representation data；
S13, the logistics representation data after step S12 lacks completion is normalized；
S14, the logistics representation data after step S13 normalized is standardized.
Further, before step S2 further include: by the essential information of analyte stream representation data, to logistics portrait number
According to classifying；Specifically: for step S1, treated that logistics representation data is for statistical analysis, records vehicle number of samples
N, data dimension m, categorical measure k；Then categorized data set is X=(U, A ∪ B), wherein U is vehicle sample set, and A indicates letter
Cease attribute set, B presentation class attribute set；
Obtain classification set: C=(X, Va, Vb), wherein VaIt is the codomain set of information attribute, VbIt is data classification attribute
Codomain set, a indicate set A in element, b expression set B in element.
Further, data grain described in step S2 is calculate by the following formula:
Ub=d (x, a)=b ∩ f (x, a), x ∈ U | a ∈ A }
Wherein, (x is a) that data classification attribute calculates function to d, and (x a) indicates that information attribute codomain calculates function, x table to f
Show the sample in vehicle sample set.
Further, classification information grain expression formula described in step S6 are as follows: Ga(b)=U | Ia(b)；Wherein, Ia(b) it indicates
Data grain UbCorresponding confidence interval.
Further, classification information set discriminant function expression formula described in step S7 are as follows:
Further, the availability calculations formula of logistics representation data described in step S7 are as follows:
VU (a)=na/n
Wherein, naIndicate the number of samples in set U comprising a.
Beneficial effects of the present invention: the pretreatment that the present invention is drawn a portrait by vehicle data, data granulation, Information Granulating etc.,
From many levels, multiple dimensions classify to representation data, and screening analyzes the data needed, and guarantees its accuracy,
Availability, and then when vehicle scheduling algorithm is when carrying out recommendation analytical calculation using these data, it is more accurate to obtain
Recommendation results human factor intervention is reduced, by truck man, freight supply to bring the significantly promotion of dispatching efficiency
Quotient and dispatching platform tripartite comprehensive coordination；By improving the availability of vehicle data, further increases subsequent vehicle scheduling and recommend
Accuracy, compared to before differentiating without addition availability of data, method of the invention makes that efficiency is recommended to improve 35% or so,
Accuracy rate improves 30% or so, and reduces for 50% recommendation time；The invention has the following advantages that
Confidence interval is introduced to the assessment of the representation data of vehicle；By structural classification confidence interval come interpretive classification information
Grain indicates a space with the division of classification confidence interval guidance, describes the unit-sized in space with granularity；It will finally count
It is divided into varigrained grain space according to collection, and constructs the assessment models of classification availability on this basis；This method for
The data predictions such as samples selection and feature selecting work important in inhibiting, and its time cost is lower, can be improved point
The learning efficiency of class system.
Detailed description of the invention
Fig. 1 is the solution of the present invention flow chart.
Specific embodiment
For convenient for those skilled in the art understand that technology contents of the invention, with reference to the accompanying drawing to the content of present invention into one
Step is illustrated.
It is as shown in Figure 1 the solution of the present invention flow chart, a kind of representation data recommended based on vehicle of the invention is available
Property judgment method, comprising the following steps:
S1, the logistics representation data of input is pre-processed, obtains the logistics representation data for complying with standard normal distribution；
Specifically include it is following step by step:
S11, value type is converted by the character type data in the logistics representation data of input；
The logistics representation data of input is pre-processed, specific content is as follows: using feature coding technology first
Character type data is quantized, logistics data there are many data types, in order to facilitate the processing of follow-up data, this implementation
The data of character type is needed to be converted into example the data of numeric type, such as the data of Boolean type are indicated with 0 or 1, such as
The data of String type can be indicated such as the starting point of transport with digital code.
S12, shortage of data completion processing is carried out to through step S11 treated logistics representation data；
Shortage of data is for data mining, the presence of null value, cause many influences, and system loss is a large amount of useful
Information；The uncertainty shown in system is more significant, and the certainty ingredient contained in system is more difficult to hold；Include null value
Data mining process can be made to fall into chaos, lead to insecure output.The record of shortage of data is by mean value completion, if empty
Value is numeric type, just fills the attribute value of the missing in the average value of the value of other all objects according to the attribute；Such as
Fruit null value is non-numeric type, just according to the mode principle in statistics, with the attribute other all objects value number
Most values (i.e. the highest value of the frequency of occurrences) carrys out the attribute value of the polishing missing.
S13, the logistics representation data after step S12 lacks completion is normalized；
For convenient data processing, data are mapped within the scope of 0-1 and are handled, it is more convenient and quick, it is convenient for different lists
The index of position or magnitude, which is able to carry out, to be compared and weights.The normalization that data are realized by arctan function, uses this method
It should be noted that then data should all be more than or equal to 0, and the data less than 0 will be mapped if it is desired to the section of mapping is [0,1]
Onto [- 1,0] section, and and the result of not all data normalization be mapped on [0,1] section.
S14, the logistics representation data after step S13 normalized is standardized.
Standardization is the column processing data according to eigenmatrix, and the method by seeking z-score is converted to standard normal
Distribution, related to whole sample distribution, each sample point can have an impact standardization.
By converting function
X=x- μ σ
X is data set, and μ is the mean value of all sample datas, and σ is the standard deviation of all sample datas, by standardized data
It just complies with standard after being handled divided by standard deviation by subtracting mean value and is just distributed very much.
S2, according to logistics representation data category division data grain；Step by step including following two:
S21, the essential information for analyzing representation data carry out classification processing to data
For step S1, treated that logistics data is for statistical analysis, records vehicle number of samples n, data dimension m, class
Other quantity k.Then categorized data set is X=(U, A ∪ B), and wherein U is vehicle sample set, and A indicates information attribute set, and B is indicated
Categorical attribute.It so can be obtained by a classification set, C=(X, Va, Vb), VaIt is the codomain set of information attribute, and f (x, a)
It is that information attribute codomain calculates function U × A=Va；VbIt is the codomain set of data classification attribute, d is classified calculating function U × B
=Vb。
S22, data grain is divided according to data category
The step S21 of division according to to(for) classification belongs to any one the category label of categorical attribute codomain set,
Division U of the available vehicle sample set U about category label bb.Then think UbFor a data grain, data grain can pass through
Ub=d (x, a)=b ∩ f (x, a), x ∈ U | a ∈ A }
It is calculated, according to Vb={ b1,b2,…,bkIt can be concluded that based on representation data divide k number according to grain, and
This k number is according to irrelevant between grain.
S3, the mean value for calculating each data grain and the difference of two squares and；
It calculates the mean value of sample: belonging to the vehicle sample a of set A for any one, calculate and marked on each attribute a
Mean value when for b
Calculate sample the difference of two squares and, calculating formula is as follows:
S4, ascending sort is carried out to the mean value of each data grain, obtains the equal value set an of ascending order, and according to every number
According to the weighted average of the equal value set of the difference of two squares and calculating ascending order of grain: Oa′(bk)=(SSTa(bk+1)*Oa(bk)+SSTa
(bk)*Oa(bk+1))/(SSTa(bk+1)+SSTa(bk)) obtain orderly weighted average value set；
According to the mean value O of each data graina(b) it is ranked up, then obtains the set O an of ascending ordera.Then O is calculateda
Weighted average, therefore obtain an orderly weighted average value set.
S5, the corresponding confidence interval of data grain is calculated；
For sample set A={ a1,a2,…,ak, Δ is given minimum number, on attribute a
Maximum
Minimum
A in the present embodiment is interpreted as an element in set A.
Therefore, a confidence interval I is obtaineda, here it is the standards evaluated sample a, with this confidence interval IaFor foundation.
S6, classification information grain is converted for data grain according to confidence interval；
Belong to class indication set V for knownbA classification information b, in the confidence interval I of sample aaIt (b) can on
It is divided with obtaining a classification information of sample set U
Ga(b)=U | Ia(b)
B in the present embodiment is understood to mean that set VbIn an element.
By Ga(b) it is known as a classification information grain.Again because of Vb={ b1,b2,…,bk, it is possible to obtain k classification letter
Cease grain.Definition: if assessment object is attribute a, then according to the set I of classification confidence intervala, an available info class:
By learning G aboveaK information can be divided, so classification information grain set GaIt can indicate are as follows:
Ga={ Ga(bi)|bi∈Vb}
S7, according to classification information set discriminant function, obtain the availability of logistics representation data.
Firstly, defining classification information aggregate discriminant function
For any vehicle sample x ∈ U in classification set C=(X, Va, Vb), attribute a ∈ A, defined function
To illustrate whether x is included in the calculated classification information grain set G of step S6aAmong.
Then, the classification availability of data set is calculated
There is that sample for known vehicle sample set U, includes the number of samples of a in statistics set U and be denoted as
The availability of so vehicle is
VU (a)=na/n
To sum up, the invention has the following advantages that
1, confidence interval is introduced to the assessment of the representation data of vehicle
In the Estimating Confidence Interval of the representation data parameter based on vehicle, σ is confidence level, under confidence level, not
Know that parameter is estimated by sample information, population parameter, which is fallen in the section, is guaranteed that σ is smaller using the probability of 1- σ, is set
Believe that section is also bigger, note 1- σ is confidence level, reflects the credibility of interval estimation.During inspection, once significant water
Flat σ and test statistics determine that boundary value obtains position and also just obtained determination.The weighting of ordered set is used in the present embodiment
Average value and the difference of two squares and the maximum and minimum in attribute a are calculated, then maximum and minimum are exactly setting for attribute a
Believe section.
2, info classization is introduced into the processing of vehicle data
For enormous amount, number is many and diverse, and data category is unobvious that vehicle transport data, the present invention use Information Granulating
Data are decomposed, are then reorganizing the data of decomposition according to the requirement of analysis.It is divided into two aspects to carry out
Tissue: one is simply decomposed according to certain natural qualities of data, is then reorganized；The other is according to work
Demand be based on frame used in project, theoretical, the characteristics of method, is decomposed and is recombinated to the data of part.To me
Can from multiple visual angles (driver information class, supplier of goods info class, information of vehicles class, driver and supplier of goods it is ripe
Know degree information class, the regional information class that driver often passes, driver preference route information class, driver's active time segment information class
Deng) pretreated vehicle representation data is handled.
Structural classification confidence interval of the present invention carrys out interpretive classification information, is indicated with the division of classification confidence interval guidance
Grain space, describes the unit-sized in space with granularity.Data set is finally divided into varigrained grain space, and herein
On the basis of construct classification availability assessment models.This method works for data predictions such as samples selection and feature selectings
Important in inhibiting, and its time cost is lower, and the learning efficiency of categorizing system can be improved.
Those of ordinary skill in the art will understand that the embodiments described herein, which is to help reader, understands this hair
Bright principle, it should be understood that protection scope of the present invention is not limited to such specific embodiments and embodiments.For ability
For the technical staff in domain, the invention may be variously modified and varied.All within the spirits and principles of the present invention, made
Any modification, equivalent substitution, improvement and etc. should be included within scope of the presently claimed invention.
Claims (7)
1. a kind of representation data availability judgment method recommended based on vehicle characterized by comprising
S1, the logistics representation data of input is pre-processed, obtains the logistics representation data for complying with standard normal distribution；
S2, according to logistics representation data category division data grain；
S3, the mean value for calculating each data grain and the difference of two squares and；
S4, ascending sort is carried out to the mean value of each data grain, the equal value set an of ascending order is obtained, according to each data grain
The weighted average of the difference of two squares and the equal value set of calculating ascending order, obtains orderly weighted average value set；
S5, according to orderly weighted average value set, calculate the corresponding confidence interval of data grain；
S6, classification information grain is converted for data grain according to confidence interval；
S7, according to classification information set discriminant function, obtain the availability of logistics representation data.
2. a kind of representation data availability judgment method recommended based on vehicle according to claim 1, which is characterized in that
Step S1 specifically include it is following step by step:
S11, value type is converted by the character type data in the logistics representation data of input；
S12, shortage of data completion processing is carried out to through step S11 treated logistics representation data；
S13, the logistics representation data after step S12 lacks completion is normalized；
S14, the logistics representation data after step S13 normalized is standardized.
3. a kind of representation data availability judgment method recommended based on vehicle according to claim 1, which is characterized in that
Before step S2 further include: by the essential information of analyte stream representation data, classify to logistics representation data；Specifically
Are as follows: for step S1, treated that logistics representation data is for statistical analysis, records vehicle number of samples n, data dimension m, class
Other quantity k；Then categorized data set is X=(U, A ∪ B), wherein U is vehicle sample set, and A indicates information attribute set, B table
Show categorical attribute set；
Obtain classification set: C=(X, Va, Vb), wherein VaIt is the codomain set of information attribute, VbIt is the value of data classification attribute
Domain set, a indicate that the element in set A, b indicate the element in set B.
4. a kind of representation data availability judgment method recommended based on vehicle according to claim 3, which is characterized in that
Data grain described in step S2 is calculate by the following formula:
Ub=d (x, a)=b ∩ f (x, a), x ∈ U | a ∈ A }
Wherein, (x is a) that data classification attribute calculates function to d, and (x a) indicates that information attribute codomain calculates function to f, and x indicates vehicle
Sample in sample set.
5. a kind of representation data availability judgment method recommended based on vehicle according to claim 4, which is characterized in that
Classification information grain expression formula described in step S6 are as follows: Ga(b)=U | Ia(b)；Wherein, Ia(b) data grain U is indicatedbCorresponding confidence
Section.
6. a kind of representation data availability judgment method recommended based on vehicle according to claim 5, which is characterized in that
Classification information set discriminant function expression formula described in step S7 are as follows:
7. a kind of representation data availability judgment method recommended based on vehicle according to claim 5, which is characterized in that
The availability calculations formula of logistics representation data described in step S7 are as follows:
VU (a)=na/n
Wherein, naIndicate the number of samples in set U comprising a.
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Application publication date: 20190816 |